FORECASTING FOR THE NEXT 3 MONTHS
#Package
library(dplyr)
library(stringr)
library(tidyr)
library(forcats)
library(ggplot2) #for basic visualization
library(modeltime) #modelling time series
library(tidymodels) #modelling time series
library(timetk) #data wrangling and visualization of time series
library(lubridate) #for work with dates and datetimes
library(tidyverse) #data wrangling
library(anomalize) #anomaly in time series
library(readr)#Open the file
sales <- read_csv("sales.csv")
products <- read_csv("products.csv")
stores <- read_csv("stores.csv")#Join all of the file, and drop the unnecessary column
Sales<-sales %>%
left_join(products, by=c("Product_ID"="Product_ID"))%>%
left_join(stores, by=c("Store_ID"="Store_ID"))
Sales<-Sales[-c(1,3,4,13)] #Drop Sale_ID, Store ID, Product ID, Store open date
Sales## # A tibble: 829,262 x 9
## Date Units Product_Name Product_Category Product_Cost Product_Price
## <date> <dbl> <chr> <chr> <chr> <chr>
## 1 2017-01-01 1 Chutes & Ladders Games $9.99 $12.99
## 2 2017-01-01 1 Action Figure Toys $9.99 $15.99
## 3 2017-01-01 1 Deck Of Cards Games $3.99 $6.99
## 4 2017-01-01 1 Dart Gun Sports & Outdoo~ $11.99 $15.99
## 5 2017-01-01 1 Lego Bricks Toys $34.99 $39.99
## 6 2017-01-01 1 Splash Balls Sports & Outdoo~ $7.99 $8.99
## 7 2017-01-01 1 Dart Gun Sports & Outdoo~ $11.99 $15.99
## 8 2017-01-01 1 Animal Figures Toys $9.99 $12.99
## 9 2017-01-01 1 Mini Ping Pong ~ Sports & Outdoo~ $6.99 $9.99
## 10 2017-01-01 1 Deck Of Cards Games $3.99 $6.99
## # ... with 829,252 more rows, and 3 more variables: Store_Name <chr>,
## # Store_City <chr>, Store_Location <chr>#Delete the “$” symbol in column product cost and product price and change to numeric type, and make new column to count the total cost and total price
Sales<-Sales%>%
mutate(Product_Cost=str_replace(Product_Cost,"\\$",""))%>%
mutate(Product_Price=str_replace(Product_Price,"\\$",""))%>%
mutate(Product_Cost=as.numeric(Product_Cost))%>%
mutate(Product_Price=as.numeric(Product_Price))%>%
mutate(Total_Cost=Units*Product_Cost)%>%
mutate(Total_Price=Units*Product_Price)
Sales## # A tibble: 829,262 x 11
## Date Units Product_Name Product_Category Product_Cost Product_Price
## <date> <dbl> <chr> <chr> <dbl> <dbl>
## 1 2017-01-01 1 Chutes & Ladders Games 9.99 13.0
## 2 2017-01-01 1 Action Figure Toys 9.99 16.0
## 3 2017-01-01 1 Deck Of Cards Games 3.99 6.99
## 4 2017-01-01 1 Dart Gun Sports & Outdoo~ 12.0 16.0
## 5 2017-01-01 1 Lego Bricks Toys 35.0 40.0
## 6 2017-01-01 1 Splash Balls Sports & Outdoo~ 7.99 8.99
## 7 2017-01-01 1 Dart Gun Sports & Outdoo~ 12.0 16.0
## 8 2017-01-01 1 Animal Figures Toys 9.99 13.0
## 9 2017-01-01 1 Mini Ping Pong ~ Sports & Outdoo~ 6.99 9.99
## 10 2017-01-01 1 Deck Of Cards Games 3.99 6.99
## # ... with 829,252 more rows, and 5 more variables: Store_Name <chr>,
## # Store_City <chr>, Store_Location <chr>, Total_Cost <dbl>, Total_Price <dbl>#Extract day and month from column Date and make new column based on it and reorder to make it nice to look
Sales<-Sales %>%
mutate(Day = lubridate::day(Date),
Month = month.name[lubridate::month(Date, label = TRUE)])
Sales<-Sales[, c(1,12,13,2,3,4,5,6,10,11,7,8,9)]
Sales## # A tibble: 829,262 x 13
## Date Day Month Units Product_Name Product_Category Product_Cost
## <date> <int> <chr> <dbl> <chr> <chr> <dbl>
## 1 2017-01-01 1 January 1 Chutes & Ladders Games 9.99
## 2 2017-01-01 1 January 1 Action Figure Toys 9.99
## 3 2017-01-01 1 January 1 Deck Of Cards Games 3.99
## 4 2017-01-01 1 January 1 Dart Gun Sports & Outdoo~ 12.0
## 5 2017-01-01 1 January 1 Lego Bricks Toys 35.0
## 6 2017-01-01 1 January 1 Splash Balls Sports & Outdoo~ 7.99
## 7 2017-01-01 1 January 1 Dart Gun Sports & Outdoo~ 12.0
## 8 2017-01-01 1 January 1 Animal Figures Toys 9.99
## 9 2017-01-01 1 January 1 Mini Ping Pong ~ Sports & Outdoo~ 6.99
## 10 2017-01-01 1 January 1 Deck Of Cards Games 3.99
## # ... with 829,252 more rows, and 6 more variables: Product_Price <dbl>,
## # Total_Cost <dbl>, Total_Price <dbl>, Store_Name <chr>, Store_City <chr>,
## # Store_Location <chr>#Check the NA rows
colSums(is.na(Sales))## Date Day Month Units
## 0 0 0 0
## Product_Name Product_Category Product_Cost Product_Price
## 0 0 0 0
## Total_Cost Total_Price Store_Name Store_City
## 0 0 0 0
## Store_Location
## 0#Lets look total Sales each day
Sales_day<-Sales%>%
group_by(Day)%>%
summarise(Total.Sales=sum(Total_Price))%>%
arrange(desc(Total.Sales))
ggplot(data=Sales_day,aes(
x=reorder(Day,Total.Sales),
y=Total.Sales))+
geom_col(aes(fill=Total.Sales),show.legend = FALSE)+
theme_bw()+
theme(axis.text=element_text(size=10),
axis.title=element_text(size=12, colour="black"))+
labs(title = "Total Sales Each Day",
subtitle = "January 2017 - September 2018",
x = "Day",
y = "Total Sales")
Look at that, Day 1, 15, 30 the first date, the middle date, the end date is it coincidence ??, nobody knows. Day 31, is the lowet because yeah, we know that not all of month has day 31
Sales%>%
group_by(Day, Product_Category)%>%
summarise(Total.Price = sum(Total_Price))%>%
ggplot(aes(
x=Day,
y=Total.Price,
fill=Product_Category))+
geom_col(position = "stack")+
theme_bw()+
theme(legend.position = "bottom",
legend.title = element_blank())+
scale_x_continuous(breaks = 1:31)+
labs(title = "Sales Composition Each Day Based on Product Category",
subtitle = "January 2017 - September 2018",
x = "Day",
y = "Total Sales")## `summarise()` has grouped output by 'Day'. You can override using the `.groups` argument.
Sales%>%
group_by(Day, Product_Category)%>%
summarise(count=n())%>%
ggplot(aes(
x=Day,
y=count,
fill=Product_Category))+
geom_col(position = "stack")+
theme_bw()+
theme(legend.position = "bottom",
legend.title = element_blank())+
scale_x_continuous(breaks = 1:31)+
labs(title = "Total Unit Sold Composition Each Day Based on Product Category",
subtitle = "January 2017 - September 2018",
x = "Day",
y = "Total Unit")## `summarise()` has grouped output by 'Day'. You can override using the `.groups` argument.
#Lets look total Sales each month
Sales_month<-Sales%>%
group_by(Month)%>%
summarise(Total.Sales=sum(Total_Price))%>%
arrange(desc(Total.Sales))
ggplot(data=Sales_month,aes(
x=reorder(Month,Total.Sales),
y=Total.Sales,
label=Total.Sales))+
geom_col(aes(fill=Total.Sales),show.legend = FALSE)+
coord_flip()+
theme_bw()+
theme(axis.text=element_text(size=10),
axis.title=element_text(size=12, colour="black"))+
geom_label(aes(fill=Total.Sales),
colour="white",
size=4,
show.legend = FALSE,
position = position_stack(0.9))+
labs(title = "Total Sales Each Month",
subtitle = "January 2017 - September 2018",
x = "Month",
y = "Total Sales")
Wow, April is the highest sales, october is the least or I can say the Q4 is the lowest because the data just to september 2018, now exclude the Q4, Q2 have the highest sales (April, May, June)
Sales%>%
group_by(Month, Product_Category)%>%
summarise(Total.Price = sum(Total_Price))%>%
ggplot(aes(
x=fct_rev(factor(Month,levels=month.name)),
y=Total.Price,
fill=Product_Category))+
geom_col(position = "stack")+
theme_bw()+
coord_flip()+
theme(legend.position = "bottom",
legend.title = element_blank())+
labs(title = "Sales Composition Each Month Based on Product Category",
subtitle = "Sales From January 2017 - September 2018",
x = "Month",
y = "Total Sales")## `summarise()` has grouped output by 'Month'. You can override using the `.groups` argument.
Sales%>%
group_by(Month, Product_Category)%>%
summarise(count=n())%>%
ggplot(aes(
x=fct_rev(factor(Month,levels=month.name)),
y=count,
fill=Product_Category))+
geom_col(position = "stack")+
theme_bw()+
coord_flip()+
theme(legend.position = "bottom",
legend.title = element_blank())+
labs(title = "Total Unit Sold Composition Each Month Based on Product Category",
subtitle = "January 2017 - September 2018",
x = "Month",
y = "Total Unit")## `summarise()` has grouped output by 'Month'. You can override using the `.groups` argument.
#Lets check the top 10 product
Sales%>%
group_by(Product_Name)%>%
summarise(Total.Price=sum(Total_Price))%>%
arrange(desc(Total.Price))%>%
head(10)%>%
ggplot(aes(
x=reorder(Product_Name,Total.Price),
y = Total.Price,
label = Total.Price))+
geom_col(aes(fill = Total.Price), show.legend = FALSE)+
coord_flip()+
theme_bw()+
theme(axis.text = element_text(size = 10),
axis.title = element_text(size = 12, colour = "black"))+
geom_label(aes(fill = Total.Price),
colour = "white",
size = 4,
show.legend = FALSE,
position = position_stack(0.9))+
labs(title = "Total Sales Based on Product Names",
subtitle = "Top 10 Product From January 2017 - September 2018",
x = "Product Names",
y = "Total Sales")
Lego Bricks has the highest total sales
#Now lets see the Store name that made the highest sales
Sales%>%
group_by(Store_Name)%>%
summarise(Total.Price=sum(Total_Price))%>%
arrange(desc(Total.Price)) %>%
head(10)%>%
ggplot(aes(
x=reorder(Store_Name,Total.Price),
y = Total.Price,
label = Total.Price))+
geom_col(aes(fill = Total.Price), show.legend = FALSE)+
coord_flip()+
theme_bw()+
theme(axis.text = element_text(size = 8),
axis.title = element_text(size = 10, colour = "black"))+
geom_label(aes(fill = Total.Price),
colour = "white",
size = 4,
show.legend = FALSE,
position = position_stack(0.9))+
labs(title = "Total Sales Based on Stores Name",
subtitle = "Top 10 Stores From January 2017 - September 2018",
x = "Store Names",
y = "Total Sales")
Maven Toys Cludad de Mexico 1 has the highest sales
#Now lets see the store location
Sales%>%
group_by(Store_Location)%>%
summarise(Total.Price=sum(Total_Price))%>%
arrange(desc(Total.Price))%>%
ggplot(aes(
x=Store_Location,
y =Total.Price,
label =round(Total.Price, digits = 2)))+
geom_col(aes(fill = Total.Price), show.legend = FALSE)+
coord_flip()+
theme_bw()+
theme(axis.text = element_text(size = 10),
axis.title = element_text(size = 12, colour = "black"))+
geom_label(aes(fill = Total.Price),
colour = "white",
size = 5,
show.legend = FALSE,
position = position_stack(.9))+
labs(title = "Store Location Total Sales",
subtitle = "January 2017 - September 2018",
x = "Location",
y = "Total Sales")
WOW, Look at that, downtown location is dominating
#PERCENTAGE
Sales%>%
select(Store_Location, Product_Category, Total_Price)%>%
group_by(Store_Location, Product_Category)%>%
summarise(Total.Price = sum(Total_Price))%>%
mutate(Percent = round(Total.Price / sum(Total.Price)*100 , digits = 2))%>%
ggplot(aes(
x=Store_Location,
y=Percent,
fill=Product_Category))+
geom_bar(stat = "identity")+
geom_text(aes(label = paste(Percent,"%"), y = Percent),
position = position_stack(vjust = 0.5))+
coord_flip()+
theme_bw()+
theme(legend.position = "bottom",
legend.title = element_blank())+
labs(title = "Sales Composition in Store Location Based on Product Category",
subtitle = "January 2017 - September 2018",
x = "Location",
y = "Percentage")## `summarise()` has grouped output by 'Store_Location'. You can override using the `.groups` argument.
Sales%>%
select(Store_Location, Product_Category, Total_Price)%>%
group_by(Store_Location, Product_Category)%>%
summarise(Total.Price = sum(Total_Price))%>%
ggplot(aes(
x=reorder(Store_Location,Total.Price),
y=Total.Price,
fill=Product_Category))+
geom_col(position = "stack")+
coord_flip()+
theme_bw()+
theme(legend.position = "bottom",
legend.title = element_blank())+
labs(title = "Sales Composition in Store Location Based on Product Category",
subtitle = "January 2017 - September 2018",
x = "Location",
y = "Total Sales")## `summarise()` has grouped output by 'Store_Location'. You can override using the `.groups` argument.
Sales%>%
select(Store_Location, Product_Category, Total_Price)%>%
group_by(Store_Location, Product_Category)%>%
summarise(count=n())%>%
ggplot(aes(
x=reorder(Store_Location,count),
y=count,
fill=Product_Category))+
geom_col(position = "stack")+
coord_flip()+
theme_bw()+
theme(legend.position = "bottom",
legend.title = element_blank())+
labs(title = "Total Unit Sold Composition in Store Location Based on Product Category",
subtitle = "January 2017 - September 2018",
x = "Location",
y = "Total Unit")## `summarise()` has grouped output by 'Store_Location'. You can override using the `.groups` argument.
#MENCOBA
Sales %>%
mutate(DayName=lubridate::wday(Date, label = TRUE))%>%
group_by(DayName,Product_Category)%>%
summarise(count=n()) %>%
ggplot(aes(
x=reorder(DayName,count),
y=count,
fill=Product_Category))+
geom_col(position = "stack")+
coord_flip()+
theme_bw()+
theme(legend.position = "bottom",
legend.title = element_blank())+
labs(title = "Total Unit Sold Composition Based on Product Category",
subtitle = "January 2017 - September 2018",
x = "Product Category",
y = "Total Unit")## `summarise()` has grouped output by 'DayName'. You can override using the `.groups` argument.
#MENCOBA
Sales %>%
mutate(DayName=lubridate::wday(Date, label = TRUE))%>%
group_by(DayName,Product_Category, Store_Location)%>%
summarise(count=n())%>%
ggplot(aes(
x=DayName,
y=count,
fill=Product_Category))+
geom_col()+
facet_wrap(~Store_Location)+
theme_bw()+
theme(legend.position = "bottom",
legend.title=element_blank())+
labs(title = "",
x = "Day Name",
y = "Total Unit")## `summarise()` has grouped output by 'DayName', 'Product_Category'. You can override using the `.groups` argument.
#sales daily time series
sales_daily<-Sales%>%
mutate(Date=floor_date(Date, unit="day"))%>%
group_by(Date)%>%
summarise(Total.Price=sum(Total_Price))
sales_daily%>%plot_time_series(Date,Total.Price,
.title = "Daily Sales January 2017 - September 2018")#check the anomaly and clear
sales_daily%>%
plot_anomaly_diagnostics(Date, Total.Price)sales_daily2<-sales_daily%>%
time_decompose(Total.Price)%>%
anomalize(remainder)%>%
clean_anomalies()%>%
select(Date, observed_cleaned)%>%
rename(Total.Price=observed_cleaned)
sales_daily2%>%plot_time_series(Date, Total.Price,
.title = "Daily Sales January 2017 - September 2018")#Check the stationary
library(tseries)
sales_daily_test=ts(sales_daily2$Total.Price, start = c(2017,1,1),frequency = 365.25)test for stationary with adf and pp
adf.test(sales_daily_test);##
## Augmented Dickey-Fuller Test
##
## data: sales_daily_test
## Dickey-Fuller = -4.1858, Lag order = 8, p-value = 0.01
## alternative hypothesis: stationarypp.test(sales_daily_test)##
## Phillips-Perron Unit Root Test
##
## data: sales_daily_test
## Dickey-Fuller Z(alpha) = -214.28, Truncation lag parameter = 6, p-value
## = 0.01
## alternative hypothesis: stationarysince in adf and pp test, p-value less than 0.05, the data is stationary
sales_daily2%>%
plot_seasonal_diagnostics(Date, Total.Price,
.feature_set = c("wday.lbl","month.lbl"))#Now lets split date train and date test, (train80% ; test20%)
splits<-initial_time_split(sales_daily2, prop = 0.8)
#you can also use this
#splits<-time_series_split(sales_daily,
# assess = "4 months",
# cumulative = TRUE) #tells the sampling to use all of the prior data as the training set.
splits%>%
tk_time_series_cv_plan()%>%
plot_time_series_cv_plan(Date,Total.Price,
.title = "Data Split into Training and Testing")#Snaive
model_snaive<-naive_reg()%>%
set_engine("snaive")%>%
fit(Total.Price~Date, training(splits))
model_snaive## parsnip model object
##
## Fit time: 90ms
## SNAIVE [7]
## --------
## Model:
## # A tibble: 7 x 2
## Date value
## <date> <dbl>
## 1 2018-05-19 36488.
## 2 2018-05-20 41634.
## 3 2018-05-21 20881.
## 4 2018-05-22 19508.
## 5 2018-05-23 20939.
## 6 2018-05-24 19333.
## 7 2018-05-25 30387.#ETS
model_ets<-exp_smoothing()%>%
set_engine("ets")%>%
fit(Total.Price~Date, training(splits))
model_ets## parsnip model object
##
## Fit time: 2s
## ETS(M,N,M)
##
## Call:
## forecast::ets(y = outcome, model = model_ets, damped = damping_ets,
##
## Call:
## alpha = alpha, beta = beta, gamma = gamma)
##
## Smoothing parameters:
## alpha = 0.1706
## gamma = 1e-04
##
## Initial states:
## l = 18177.9105
## s = 1.3066 1.0586 0.8522 0.8276 0.7561 0.8397
## 1.3593
##
## sigma: 0.1629
##
## AIC AICc BIC
## 11486.94 11487.38 11529.28#ARIMA
model_arima<-arima_reg()%>%
set_engine("auto_arima")%>%
fit(Total.Price~Date, training(splits))
model_arima## parsnip model object
##
## Fit time: 1.6s
## Series: outcome
## ARIMA(1,0,1)(1,1,1)[7]
##
## Coefficients:
## ar1 ma1 sar1 sma1
## 0.9492 -0.7739 -0.1426 -0.8474
## s.e. 0.0294 0.0600 0.0513 0.0344
##
## sigma^2 estimated as 12476397: log likelihood=-4825.85
## AIC=9661.69 AICc=9661.82 BIC=9682.8#Prophet
model_prophet<-prophet_reg(
seasonality_yearly = TRUE)%>%
set_engine("prophet")%>%
fit(Total.Price~Date, training(splits))
model_prophet## parsnip model object
##
## Fit time: 4.2s
## PROPHET Model
## - growth: 'linear'
## - n.changepoints: 25
## - changepoint.range: 0.8
## - yearly.seasonality: 'TRUE'
## - weekly.seasonality: 'auto'
## - daily.seasonality: 'auto'
## - seasonality.mode: 'additive'
## - changepoint.prior.scale: 0.05
## - seasonality.prior.scale: 10
## - holidays.prior.scale: 10
## - logistic_cap: NULL
## - logistic_floor: NULL
## - extra_regressors: 0PENJELASAN: lihat di book modeltime
#modeltimetable
model_tbl<-modeltime_table(
model_snaive,
model_ets,
model_arima,
model_prophet)
model_tbl## # Modeltime Table
## # A tibble: 4 x 3
## .model_id .model .model_desc
## <int> <list> <chr>
## 1 1 <fit[+]> SNAIVE [7]
## 2 2 <fit[+]> ETS(M,N,M)
## 3 3 <fit[+]> ARIMA(1,0,1)(1,1,1)[7]
## 4 4 <fit[+]> PROPHET#calibrate
calibrate_tbl<-model_tbl%>%
modeltime_calibrate(testing(splits))
calibrate_tbl## # Modeltime Table
## # A tibble: 4 x 5
## .model_id .model .model_desc .type .calibration_data
## <int> <list> <chr> <chr> <list>
## 1 1 <fit[+]> SNAIVE [7] Test <tibble [128 x 4]>
## 2 2 <fit[+]> ETS(M,N,M) Test <tibble [128 x 4]>
## 3 3 <fit[+]> ARIMA(1,0,1)(1,1,1)[7] Test <tibble [128 x 4]>
## 4 4 <fit[+]> PROPHET Test <tibble [128 x 4]>~Calibration is how confidence intervals and accuracy metrics are determined. ~Calibration Data is simply forecasting predictions and residuals that are calculated from out-of-sample data. ~After calibrating, the calibration data follows the data through the forecasting workflow.
#accuracy
calibrate_tbl%>%modeltime_accuracy()## # A tibble: 4 x 9
## .model_id .model_desc .type mae mape mase smape rmse rsq
## <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 SNAIVE [7] Test 4348. 19.8 0.778 17.6 5522. 0.700
## 2 2 ETS(M,N,M) Test 3870. 19.3 0.692 16.8 4689. 0.721
## 3 3 ARIMA(1,0,1)(1,1,1)[7] Test 3965. 19.2 0.709 16.8 4917. 0.712
## 4 4 PROPHET Test 3172. 15.1 0.567 14.0 3953. 0.733#Test Set Visualization
calibrate_tbl%>%
modeltime_forecast(
new_data = testing(splits),
actual_data = sales_daily2)%>%
plot_modeltime_forecast()%>%
plotly::layout(
legend=list(
orientation="h",
xanchor="center",
x=0.5,
y=-0.2))#FORECAST FUTURE
future_forecast_tbl<-calibrate_tbl%>%
modeltime_refit(sales_daily2)%>% #retrain on full data
modeltime_forecast(
h = "3 months",
actual_data = sales_daily2)
future_forecast_tbl%>%
plot_modeltime_forecast()%>%
plotly::layout(
legend=list(
orientation="h",
xanchor="center",
x=0.5,
y=-0.2))#Forecast with prophet
model_prophet2<-prophet_reg(
growth = "linear", #see the trend
prior_scale_changepoints = 0.4, #Parameter modulating the flexibility of the automatic changepoint selection
prior_scale_seasonality = 5, #modulating the strength of the seasonality model
seasonality_yearly = TRUE,
seasonality_weekly = TRUE,
seasonality_daily = FALSE)%>%
set_engine("prophet")%>%
fit(Total.Price~Date, training(splits))
model_prophet2## parsnip model object
##
## Fit time: 350ms
## PROPHET Model
## - growth: 'linear'
## - n.changepoints: 25
## - changepoint.range: 0.8
## - yearly.seasonality: 'TRUE'
## - weekly.seasonality: 'TRUE'
## - daily.seasonality: 'FALSE'
## - seasonality.mode: 'additive'
## - changepoint.prior.scale: 0.4
## - seasonality.prior.scale: 5
## - holidays.prior.scale: 10
## - logistic_cap: NULL
## - logistic_floor: NULL
## - extra_regressors: 0model_tbl2<-modeltime_table(
model_prophet,
model_prophet2)
calibrate_tbl2<-model_tbl2%>%
modeltime_calibrate(testing(splits))
calibrate_tbl2%>%modeltime_accuracy()## # A tibble: 2 x 9
## .model_id .model_desc .type mae mape mase smape rmse rsq
## <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 PROPHET Test 3172. 15.1 0.567 14.0 3953. 0.733
## 2 2 PROPHET Test 3185. 14.0 0.570 13.9 3957. 0.737calibrate_tbl2%>%
modeltime_forecast(
new_data = testing(splits),
actual_data = sales_daily2)%>%
plot_modeltime_forecast(.title = "Test Plot")%>%
plotly::layout(
legend=list(
orientation="h",
xanchor="center",
x=0.5,
y=-0.2))future_forecast_tbl2<-calibrate_tbl2%>%
modeltime_refit(sales_daily2)%>% #retrain on full data
modeltime_forecast(
h = "3 months",
actual_data = sales_daily2)
future_forecast_tbl2%>%
plot_modeltime_forecast(.title = "Forecast 3 Months")%>%
plotly::layout(
legend=list(
orientation="h",
xanchor="center",
x=0.5,
y=-0.2))